Identification of vehicles using computer-implemented image analysis is used in various applications. For example, license plate image recognition systems are frequently used in automated tollgates that identify moving vehicles to automatically collect tolls. In addition, systems that combine license plate recognition and detection of vehicle features, such as car color or make, are used to improve identification accuracy when the license plate cannot be adequately identified. Indeed, highly accurate vehicle identification based on only vehicle features is possible with techniques based on machine-learning algorithms such as convolutional neural networks. These algorithms capture features from images of known vehicles and then identify an unknown vehicle in an image by correlating image features. Although these techniques may be computer-intensive, they can improve identification accuracy and facilitate the automation of a vehicle image identification system.
Traditionally, image recognition of vehicles has focused on non-portable applications, such as monitoring the entrance of a parking lot or supervising highway entrances. However, identification of vehicles using mobile or handheld devices is highly desirable for portable applications. For example, when a person sees a vehicle in the street and wants to get information about it, this person would normally need to approach the vehicle to figure out details such as make or model. Alternatively, the person may need to browse multiple images and websites to try to find the vehicle characteristics. Portable identification of vehicles using image analysis would facilitate identification of vehicles without the burden of approaching the vehicle or perform later searches.
Moreover, identification of vehicles using handheld devices is also desirable to exploit the handheld device capabilities and develop new applications. For example, portable identification of vehicles may enable augmented reality applications that improve user experience. After a vehicle is automatically identified, it may be possible to generate and superimpose a computer-generated image on a user's view in a client device to seamlessly provide information about the vehicle. Also, portable detection of vehicles using handheld devices may enable advertising opportunities. For example, a customer may use the handheld device to identify vehicles that the user is interested to purchase. After identifying the vehicle, the user may receive information about the vehicle and location information about car dealers selling the vehicle. Thus, portable image recognition of vehicles enables new desirable applications.
However, identification of vehicles using image analysis in handheld devices has multiple technical challenges. First, the machine-learning methods that provide enough precision for image analysis are computer-intensive and may be difficult to perform in a handheld device. These methods normally require the analysis of a plethora of well-curated training images before an identification process may be performed. Constructing a group of images that can be used to train machine-learning algorithms is difficult and resource intensive. Second, portable image identification of images needs to be performed quickly to be user-friendly. Particularly for augmented reality applications, in which users are expecting an immediate response, it is imperative to have efficient computing identification methods and communication systems that facilitate image recognition. Third, images or video feeds taken with a mobile device may not be uniform and may have different qualities and/or formats. Because image analysis using machine-learning methods is heavily dependent on the quality of the target image, accurate identification of vehicles in images taken with mobile devices is difficult and sometimes unsuccessful. Fourth, handheld devices have limited display screen space. Applications that require both image acquisition and display of information in a single screen, such as augmented reality applications, require specific graphical user interfaces so the user can comfortably see and manipulate the information.
The disclosed machine-learning artificial intelligence system and identification methods address one or more of the problems set forth above and/or other problems in the prior art.